This notebook contains the steps and code to demonstrate support of AutoAI experiments with bias detection/mitigation in Watson Machine Learning service. It introduces commands for data retrieval, training experiments, persisting pipelines, testing pipelines and scoring.
Some familiarity with Python is helpful. This notebook uses Python 3.9.
The learning goals of this notebook are:
This notebook contains the following parts:
Before you use the sample code in this notebook, you must perform the following setup tasks:
Authenticate the Watson Machine Learning service on IBM Cloud. You need to provide platform api_key and instance location.
You can use IBM Cloud CLI to retrieve platform API Key and instance location.
API Key can be generated in the following way:
ibmcloud login
ibmcloud iam api-key-create API_KEY_NAME
In result, get the value of api_key from the output.
Location of your WML instance can be retrieved in the following way:
ibmcloud login --apikey API_KEY -a https://cloud.ibm.com
ibmcloud resource service-instance WML_INSTANCE_NAME
In result, get the value of location from the output.
Tip: Your Cloud API key can be generated by going to the Users section of the Cloud console. From that page, click your name, scroll down to the API Keys section, and click Create an IBM Cloud API key. Give your key a name and click Create, then copy the created key and paste it below. You can also get a service specific url by going to the Endpoint URLs section of the Watson Machine Learning docs. You can check your instance location in your Watson Machine Learning (WML) Service instance details.
You can also get service specific apikey by going to the Service IDs section of the Cloud Console. From that page, click Create, then copy the created key and paste it below.
Action: Enter your api_key and location in the following cell.
api_key = 'PASTE YOUR PLATFORM API KEY HERE'
location = 'PASTE YOUR INSTANCE LOCATION HERE'
wml_credentials = {
"apikey": api_key,
"url": 'https://' + location + '.ml.cloud.ibm.com'
}
!pip install -U ibm-watson-machine-learning | tail -n 1
!pip install -U autoai-libs | tail -n 1
!pip install scikit-learn==1.0.2 | tail -n 1
!pip install wget | tail -n 1
!pip install -U 'lale[fairness]' | tail -n 1
First of all, you need to create a space that will be used for your work with AutoAI. If you do not have space already created, you can use Deployment Spaces Dashboard to create one.
space_id and paste it belowAction: assign space ID below
space_id = 'PASTE YOUR SPACE ID HERE'
You can use list method to print all existing spaces.
client.spaces.list(limit=10)
from ibm_watson_machine_learning import APIClient
client = APIClient(wml_credentials)
client.set.default_space(space_id)
'SUCCESS'
cos_credentials = client.spaces.get_details(space_id=space_id)['entity']['storage']['properties']
filename = 'german_credit_data_biased_training.csv'
datasource_name = 'bluemixcloudobjectstorage'
bucketname = cos_credentials['bucket_name']
Download training data from git repository.
import wget
import os
url = "https://github.com/IBM/watson-machine-learning-samples/raw/master/cloud/data/bias/german_credit_data_biased_training.csv"
if not os.path.isfile(filename):
wget.download(url)
conn_meta_props= {
client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {datasource_name} ",
client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(datasource_name),
client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
client.connections.ConfigurationMetaNames.PROPERTIES: {
'bucket': bucketname,
'access_key': cos_credentials['credentials']['editor']['access_key_id'],
'secret_key': cos_credentials['credentials']['editor']['secret_access_key'],
'iam_url': 'https://iam.cloud.ibm.com/identity/token',
'url': cos_credentials['endpoint_url']
}
}
conn_details = client.connections.create(meta_props=conn_meta_props)
Creating connections... SUCCESS
Note: The above connection can be initialized alternatively with api_key and resource_instance_id.
The above cell can be replaced with:
conn_meta_props= {
client.connections.ConfigurationMetaNames.NAME: f"Connection to Database - {db_name} ",
client.connections.ConfigurationMetaNames.DATASOURCE_TYPE: client.connections.get_datasource_type_uid_by_name(db_name),
client.connections.ConfigurationMetaNames.DESCRIPTION: "Connection to external Database",
client.connections.ConfigurationMetaNames.PROPERTIES: {
'bucket': bucket_name,
'api_key': cos_credentials['apikey'],
'resource_instance_id': cos_credentials['resource_instance_id'],
'iam_url': 'https://iam.cloud.ibm.com/identity/token',
'url': 'https://s3.us.cloud-object-storage.appdomain.cloud'
}
}
conn_details = client.connections.create(meta_props=conn_meta_props)
from ibm_watson_machine_learning.helpers import DataConnection, S3Location
connection_id = client.connections.get_uid(conn_details)
credit_risk_conn = DataConnection(
connection_asset_id=connection_id,
location=S3Location(bucket=bucketname,
path=filename))
credit_risk_conn.set_client(client)
training_data_reference=[credit_risk_conn]
credit_risk_conn.write(data=filename, remote_name=filename)
Fairness Attribute - Bias or fairness is typically measured using some fairness attribute such as Gender, Ethnicity, Age, etc.
Monitored/Reference Group - Monitored group are those values of fairness attribute for which we want to measure bias. The rest of the values of the fairness attributes are called as reference group. In case of Fairness Attribute=Gender, if we are trying to measure bias against females, then Monitored group is “Female” and Reference group is “Male”.
Favourable/Unfavourable outcome - An important concept in bias detection is that of favourable and unfavourable outcome of the model. E.g., Claim approved can be considered as a favourable outcome and Claim denied can be considered as an unfavourable outcome.
Disparate Impact - metric used to measure bias (computed as the ratio of percentage of favourable outcome for the monitored group to the percentage of favourable outcome for the reference group). Bias is said to exist if the disparate impact value is below some threshold.
Provide input information for AutoAI optimizer:
name - experiment nameprediction_type - type of the problemprediction_column - target column namefairness_info - bias detection configurationscoring - accuracy_and_disparate_impact combined optimization metric for both accuracy and fairness. For regression learning problem the r2_and_disparate_impact metric is supported (combines r2 and fairness).fairness_info definition:¶protected_attributes (list of dicts) – subset of features for which fairness calculation is desired.
feature - name of feature for which reference_group and monitored_group are specified.reference_group and monitored_group - monitored group are those values of fairness attribute for which we want to measure bias. The rest of the values of the fairness attribute are reference group.favorable_labels and unfavorable_labels – label values which are considered favorable (i.e. “positive”). unfavorable_labels are required when prediction type is regression.
Examples of supported configuration:
fairness_info = {
"protected_attributes": [
{"feature": "Age", "reference_group": [[26, 26], [30, 75]],
"monitored_group": [[18, 25], [27, 29]]}
],
"favorable_labels": ["No Risk"]
}
fairness_info = {
"protected_attributes": [
{"feature": "sex", "reference_group": ['male', 'not specified'],
"monitored_group": ['female']},
{"feature": "age", "reference_group": [[26, 100]], "monitored_group": [[18, 25], [27, 29]]}
],
"favorable_labels": [[5000.01, 9000]],
"unfavorable_labels": [[0, 5000], [9000, 1000000]]
}
fairness_info = {
"protected_attributes": [
{"feature": "Sex", "reference_group": ['male'], "monitored_group": ['female']},
{"feature": "Age", "reference_group": [[26, 75]], "monitored_group": [[18, 25]]}
],
"favorable_labels": ["No Risk"],
"unfavorable_labels": ["Risk"],
}
from ibm_watson_machine_learning.experiment import AutoAI
experiment = AutoAI(wml_credentials, space_id=space_id)
pipeline_optimizer = experiment.optimizer(
name='Credit Risk Prediction and bias detection - AutoAI',
prediction_type=AutoAI.PredictionType.BINARY,
prediction_column='Risk',
scoring='accuracy_and_disparate_impact',
fairness_info=fairness_info,
max_number_of_estimators = 1,
retrain_on_holdout=False
)
Call the fit() method to trigger the AutoAI experiment. You can either use interactive mode (synchronous job) or background mode (asychronous job) by specifying background_model=True.
run_details = pipeline_optimizer.fit(
training_data_reference=training_data_reference,
background_mode=False)
Training job 4f4f9870-246a-4d4d-ab05-e1eddd40af00 completed: 100%|████████| [05:31<00:00, 3.31s/it]
You can use the get_run_status() method to monitor AutoAI jobs in background mode.
Download and reconstruct a scikit-learn pipeline model object from the AutoAI training job.
experiment_summary = pipeline_optimizer.summary()
experiment_summary.head()
| Enhancements | Estimator | training_disparate_impact_Sex | training_disparate_impact | holdout_accuracy_and_disparate_impact | training_roc_auc | holdout_disparate_impact_Sex | training_average_precision | training_accuracy_and_disparate_impact_(optimized) | training_log_loss | ... | holdout_balanced_accuracy | training_recall | holdout_log_loss | training_accuracy | holdout_disparate_impact | holdout_roc_auc | training_balanced_accuracy | holdout_disparate_impact_Age | training_f1 | training_disparate_impact_Age | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pipeline Name | |||||||||||||||||||||
| Pipeline_3 | HPO, FE | SnapDecisionTreeClassifier | 1.118238 | 2.081132 | 0.148464 | 0.683937 | 1.171647 | 0.476717 | 0.258060 | 9.763075 | ... | 0.702925 | 0.582514 | 9.413493 | 0.717333 | 1.653118 | 0.641539 | 0.683937 | 1.533538 | 0.580104 | 2.036895 |
| Pipeline_4 | HPO, FE | SnapDecisionTreeClassifier | 1.118238 | 2.081132 | 0.148464 | 0.683937 | 1.171647 | 0.476717 | 0.258060 | 9.763075 | ... | 0.702925 | 0.582514 | 9.413493 | 0.717333 | 1.653118 | 0.641539 | 0.683937 | 1.533538 | 0.580104 | 2.036895 |
| Pipeline_1 | SnapDecisionTreeClassifier | 1.163020 | 1.946575 | 0.198434 | 0.691364 | 1.045489 | 0.484200 | 0.130193 | 9.601894 | ... | 0.678937 | 0.598363 | 10.105659 | 0.722000 | 1.526765 | 0.684961 | 0.691364 | 1.472074 | 0.590830 | 2.038237 | |
| Pipeline_2 | HPO | SnapDecisionTreeClassifier | 1.163020 | 1.946575 | 0.198434 | 0.691364 | 1.045489 | 0.484200 | 0.130193 | 9.601894 | ... | 0.678937 | 0.598363 | 10.105659 | 0.722000 | 1.526765 | 0.684961 | 0.691364 | 1.472074 | 0.590830 | 2.038237 |
4 rows × 22 columns
pipeline_name = experiment_summary.index[experiment_summary.holdout_disparate_impact.argmax()]
best_pipeline = pipeline_optimizer.get_pipeline(pipeline_name=pipeline_name)
best_pipeline.visualize()
Each node in the visualization is a machine-learning operator (transformer or estimator). Each edge indicates data flow (transformed output from one operator becomes input to the next). The input to the root nodes is the initial dataset and the output from the sink node is the final prediction. When you hover the mouse pointer over a node, a tooltip shows you the configuration arguments of the corresponding operator (tuned hyperparameters). When you click on the hyperlink of a node, it brings you to a documentation page for the operator.
X_train, X_holdout, y_train, y_holdout = pipeline_optimizer.get_data_connections()[0].read(with_holdout_split=True)
For detail description of used metrics you can check the documentation:
from lale.lib.aif360 import disparate_impact, accuracy_and_disparate_impact
from sklearn.metrics import accuracy_score
predicted_y = best_pipeline.predict(X_holdout.values)
disparate_impact_scorer = disparate_impact(**fairness_info)
accuracy_disparate_impact_scorer = accuracy_and_disparate_impact(**fairness_info)
print("Accuracy: {:.2f}".format(accuracy_score(y_true= y_holdout, y_pred=predicted_y)))
print("Disparate impact: {:.2f}".format(disparate_impact_scorer(best_pipeline, X_holdout, y_holdout)))
print("Accuracy and disparate impact: {:.2f}".format(accuracy_disparate_impact_scorer(best_pipeline, X_holdout, y_holdout)))
Accuracy: 0.73 Disparate impact: 1.65 Accuracy and disparate impact: 0.15
You can analize favorable outcome distributions using visualize method from utils module.
from ibm_watson_machine_learning.utils.autoai.fairness import visualize
visualize(run_details, pipeline_name)
If you want to clean up all created assets:
please follow up this sample notebook.
You successfully completed this notebook!
As a next step you can deploy and score the model: Sample notebook.
Check out our Online Documentation for more samples, tutorials, documentation, how-tos, and blog posts.
Lukasz Cmielowski, PhD, is an Automation Architect and Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.
Dorota Lączak, software engineer in Watson Machine Learning at IBM
Szymon Kucharczyk, software engineer in Watson Machine Learning at IBM
Copyright © 2021 IBM. This notebook and its source code are released under the terms of the MIT License.